Unlocking Insights: Data Science & Data Analysis Expertise
decision-making in any industry.
Data Science & Data Analytics Training in Mumbai
Etlhive offers Data Science Course in Pune, includes detail Data science, Data analytics courses for IT & NON IT background students
We have wide range of tools frequently used for Data Analytics. As a part of this Data science training in Pune, we teach Data Science, Machine Learning, Deep Learning and Artificial Intelligence will be taught to you in a detailed manner. Primary goal is for students to develop ability to crack interviews for Data Science positions.
Course highlights:
- Python Programming(Basic and Advanced)
- Data manipulation with pandas & numpy and Data visualisation with Tableau
- Machine Learning with scikit, sklearn, scipy and statsmodels.
- Web scraping and Time Series forecasting
- Text processing, NLP, Image processing with Neural Networks(ANN,RNN,CNN etc.)
- Resume | Interview | Certification preparation for IABAC and IBM certification.
Syllabus
- Defining Python
- History of Python and its Growing Popularity
Features of Python and its Wide Functionality - Python 2 vs Python 3
- Setting Up Python
- Environment for Development
- What and How of Python Installation?
- IDEs: IDLE, Pycharm, and Jupyter
- Writing First Python Program
- Python Scripts on UNIX and Windows
- Installation on Ubuntu-based Machines
- Programming on Interactive Shell
- Python Identifiers and Keywords
- Indentation in Python
- Comments and Writing to the Screen
- Command Line Arguments and Flow Control
- User Input
- Python Core Objects
- Defining Built-in Functions
- Objectives
- Variables and their types
- Variables – String Variables
- Variables – Numeric Types
- Variables – Boolean Variables
- Boolean Object and None Object
- Tuple Object and Operations
- Dictionary Object and Operations
- Types of Variables – Dictionary
- Comparison of Variables
- Dictionary Methods and Manipulations
- Operators and Logical Operators
- Data Structures and Data Processing
- Arithmetic Operations on Numeric Values
- Operators and Keywords for Sequences
- Understanding Conditional Statements
- Break Statements and Continue Statements
- Using Indentations for defining if & else block
- Loops in Python
- While, Nested, Demo-Create
- How to Control Loops?
- Sequence and Iterable Objects
- Objectives of Functions
- Types of Functions
- Creating UDF Functions
- Function Parameters
- Unnamed and Named Parameters
- Creating and Calling Functions
- Python user Defined Functions
- Python packages Functions
- Anonymous Lambda Function
- Understanding String Object Functions
- List and Tuple Object Functions
- Studying Dictionary Object Functions
- Defining Python Inbuilt Modules
- Studying Types of Modules
- os, sys, time, random, datetime, zip modules
- How to Create Python User Defined Modules?
- Understanding Pythonpath
- Creating Python Packages
- init File and Package Initialization
- What and How of File Handling with Python?
- How to Process Text Files using Python?
- Read/Write and Append File Object
- Test Operations: os.path
- Overview of Object Oriented Programming
- Defining Classes, Objects, and Initializers
- Attributes – Built-In Class
- Destroying Objects
- Methods – Instance, Class, Static, Private methods, and Inheritance
- Data Hiding
- Module Aliases and reloading modules
- Python Exceptions Handling
- Standard Exception Hierarchy
- .. except…else
- .. finally…clause
- Creating Self-Exception Class
- User-defined Exceptions
- Debugging Errors – Unit Tests
- Project Skeleton
- Creating and Using the Skeleton
- How to use pdb debugger?
- Using Pycharm Debugger
- Asserting Statement for Debugging
- Using UnitTest Framework for Testing
- Understanding Regular Expressions
- Match Function, Search Function, and the Comparision
- Compile and Match, Match and Search
- Search and Replace
- What and How of Extended Regular Expressions?
- Wildcard Characters
- Objectives of Functions
- Types of Functions
- Creating UDF Functions
- Function Parameters
- Unnamed and Named Parameters
- Creating and Calling Functions
- Python user Defined Functions
- Python packages Functions
- Anonymous Lambda Function
- Understanding String Object Functions
- List and Tuple Object Functions
- Studying Dictionary Object Functions
- Defining Python Inbuilt Modules
- Studying Types of Modules
- os, sys, time, random, datetime, zip modules
- How to Create Python User Defined Modules?
- Understanding Pythonpath
- Creating Python Packages
- init File and Package Initialization
- What and How of File Handling with Python?
- How to Process Text Files using Python?
- Read/Write and Append File Object
- Test Operations: os.path
- Overview of Object Oriented Programming
- Defining Classes, Objects, and Initializers
- Attributes – Built-In Class
- Destroying Objects
- Methods – Instance, Class, Static, Private methods, and Inheritance
- Data Hiding
- Module Aliases and reloading modules
- Python Exceptions Handling
- Standard Exception Hierarchy
- .. except…else
- .. finally…clause
- Creating Self-Exception Class
- User-defined Exceptions
- Debugging Errors – Unit Tests
- Project Skeleton
- Creating and Using the Skeleton
- How to use pdb debugger?
- Using Pycharm Debugger
- Asserting Statement for Debugging
- Using UnitTest Framework for Testing
- Understanding Regular Expressions
- Match Function, Search Function, and the Comparision
- Compile and Match, Match and Search
- Search and Replace
- What and How of Extended Regular Expressions?
- Wildcard Characters
- Data Visualization and Matplotlib, seaborn
- Python Libraries
- Features of Matplotlib
- Line Properties Plot with (x, y)
- Set Axis, Labels, and Legend Properties
- Alpha and Annotation
- Univariate plots
- Bivariate plots
- Multivariate plots
- Interpretations
• Data Manipulation and Machine Learning with Python
• Data Manipulation with Python – Pandas
• Understanding Pandas
• Defining Data Structures
• Data Operations(filtering, sorting, grouping, aggregation, merging) and Data Standardization
• Pandas: File Read and Write Support
• SQL Operations(pandasql)
• Exploring and Understanding Data
• Exploring Numeric Variables
• Understanding Types of Data
• Qualitative and Quantitative Analysis
• Studying Descriptive Statistics
• Exploring Numeric Variables
• Measuring the Central Tendency – The Model
• Measuring Spread – Variance and Standard Deviation
• Visualising Numeric Variables – Boxplots and Histograms
• Understanding Numeric Data – Uniform and Normal Distributions
• Measuring the Central Tendency – The Mode
• Exploring Relationships between Variables
• Visualizing Relationships – Scatterplots
• Nominal and Ordinal Measurement
• Interval and Ratio Measurement
• Statistical Investigation
• Inferential Statistics
• Probability and Central Limit Theorem
• Exploratory Data Analysis
• Normal Distribution
• Distance Measures
• Euclidean & Manhattan Distance
• Minkowski & Mahalanobis
• Cosine
• Correlation
• PPMC (Pearson Product Moment Correlation)
• Importance of Hypothesis Testing in Business
• Null and Alternate Hypothesis
• Understanding Types of Errors
• Contingency Table and Decision Making
• Confidence Coefficient
• Upper Tail Test
• Understanding Parametric Tests
• Z-Test and Z-Test in R
• Chi-Square Test
• Degree of Freedom
• One-Way ANOVA Test
• F-Distribution, F-Ration Test
Regression Methods for Forecasting Numeric Data
• Understanding Neural Networks
• From Biological to Artificial Neurons
• Activation Functions
Deep Learning – Neural Networks and Support Vector Machines
• What is Regression?
• Model Selection
• Generalized Regression
• Simple Linear Regression
• Multiple Linear Regression
• Correlations
• Correlation between X and Y
• Ridge and Regularized Regression
• LASSO
• Time Series
• Prediction: Time Dependent/Variant Data
• Ordinary Least Square Regression Model
• Dummy Variable Regression Model
• Interaction Regression Model
• Non-Linear Regression Model
• Perform Regression Analysis with Multiple Variables
• Network Topology
• Recurrent and Gaussian Neural Network
• The Number of Layers
• The Direction of Information Travel
• The Number of Nodes in Each Layer
• Training Neural Networks with Backpropagation
• Support Vector Machines
• Classification with Hyperplanes
• Finding the Maximum Margin
• The Case of Linearly Separable Data
• The Case of Non-Linearly Separable Data
• Retrieve Data using SQL Statements
• Using Kernels for Non-Linear Spaces
Classification
• K-NN, Naïve Bayes, Support Vector Machines
• Defining Classification
• Understanding Classification and Prediction
• Decision Tree Classifier
• How to Build Decision Trees?
• Basic Algorithm for a Decision Tree
• Decision Trees and Data Mining
• Random Forest Classifier
• Features of Random Forests
• Out of Box Error Estimate and Variable Importance
• Naïve Bayes Classifier Model
• Bayesian Theorem
Advantages and Disadvantages of Naïve Bayes Classifier Model
• Understanding Support Vector Machines
• Understanding Linear SVMs
• Logistic Regression
• Bagging and Boosting(Adaboost)
• Understanding K-means Clustering
• K-means and Pseudo Code
• K-means Clustering using R
• TF-IDF and Cosine Similarity
• Application to Vector Space Model
• What is Hierarchical Clustering?
• Hierarchical Clustering Algorithm
• Understanding Agglomerative Clustering Process
• DBSCAN Clustering
• What is Association Rule Mining?
• Association Rule Strength Measures
• Checking Apriori Algorithms
• Ordering Items
• Understanding Candidate Generation
• Performing Visualisation on Associated Rules
• Dimensionality reduction
1. Introduction to Artificial Intelligence
2. History of Arificial Intelligence
3. Future and Market Trends in Artificial Intelligence
4. Neural
Network and Perceptron
5. Understanding Feedforward Networks
6. Exploring Backpropagation
7. Understanding Sensor Processing
8. Studying Neural Elements
1. Creating Database, using Database
2. Creating Tables, inserting Values in the Table
3. Select Query where clauses
4. And, Or, Not Operators
5. In Operators, Not In Operators
6. Between & Not Between Operators
7. Like Operators orders distinct Command
8. Distinct Clauses
9. Limit Clauses with where Clauses
1. Offset Arithmetic expression (SUM, AVG, MAX, MIN, COUNT) Update Functions
2. Inner, Left, Right, Outer, Fuller, Cross Join
3. Group By, Having clauses, Sub Query
4. Advanced Sub query using with Functions
5. Union And Union All
6. Advanced Union & Union all with Functions
7. Advanced Union & Union all using Where Clauses
8. Advanced Union & Union all using Joins
9. If And Case Statements
10. STRING FN & manipulation of STRINGS VALUE using String fn
11. Date Function
12. Date Format Functions
13. Delete And Truncate
14. Alter
15. Creating View Advanced concept
16. Procedure.
1. Power BI Introduction
2. Introduction to Power BI
3. Desktop, Getting Data (Excel and RDBMS, Web, SharePoint), Naming for Q&A,
4. Direct Query vs Import Data
5. Introduction to Modelling
6. Set Up and Manager
7. Relationships Cardinality and Cross Filtering
8. Creating Hierarchy in the Model
9. Introduction to Modelling
10. Default Summarization and Sort by Creating Calculated Columns
11. Creating Measures & Quick Measures
12. Creating Visuals Colour and Conditional Formatting
13. Setting Sort Order
14. Scatter and Bubble
15. Charts and Play Axis
16. Tool Tips, Slicers
17. Timeline Slicers and Sync Slicers
18. Cross Filtering and Highlighting
19. Creating Visuals Visual
20. Page & Report Level Filters
21. Drill Down/Up Hierarchies
22. Constant Lines Tables, Matrix and Table
23. Conditional Formatting KPI’s, Cards and Gauges
24. Map
25. Visualizations
26. Custom Visuals
27. Creating Visuals
28. Managing and Arranging
29. Drill Through
30. Custom Report Themes
31. Grouping and Binning
32. Bookmark & Buttons
33. DAX Expressions
34. Introduction to Modelling
35. Set Up and Manager
36. Relationships Cardinality and Cross Filtering
37. Creating Hierarchy in the Model
38. Introduction to Modelling
39. Default Summarization and Sort by
40. Creating Calculated Columns
41. Creating Measures & Quick Measures
42. Publishing and Sharing
43. Sharing Options
44. Publish from Power BI
45. Desktop Publish Reports to Web
46. Sharing Reports and Dashboards Workspaces
47. Apps. Sharing Options
48. Printing PDF’s and
49. Exports Row Level Security
50. Exporting Data from Visualization Refreshing
51. Datasets Understanding
52. Data Refresh Gateways
1. Excel and basic formula
2. Introduction to excel and its interface
3. Entering and editing data into cells & autofill
4. Basic Formating using shortcuts keys
5. Basic DAX – sum, Average,Count,
6. Sorting and filtering the data with accending & Decending Inserting
7. deleting and renaming ther worksheet
8. Ranges selection using range name for easier references
9. Conditional Formating
1. Logical Dax: If statements nested ifs, And & or operators
2. Lookup Functions: Vlookup Dax & Hlookup
3. AX for searching and retreiving data
4. Text DAX: Concatenation, LEFT, RIGHT,MID DATE & Time Function: Working with Date
using DAX
5. Introduction Pivot Creating PIVOT to summarise and analyse large dataset
6. Pivot table filtering and formating
7. Calculated fields in pivot table pivot charts Creating
8. Charts in excel
9. Chart formatting chart type and customization
10. Data analysis using chart Data
11. Validation:Setting data validation rules
12. Scenario Manager:Creating and managing scenarios
13. Text to columns: Splitting text into multiple columns using delimeters
14. Removing Duplicates Data
15. Consolidation Text
16. Functions for data using Trim , proper, upper, lower
17. What if Analysis:using tables for scenarios
18. Power Query: Importing transforming bigdata
19. Macros:Recording and running macros to automate task
20. Customizing Ribbon: Modifying the ribbon
21. Introduction to VBA
22. Enablind Developer tab
23. VBA Editor : Opening and Navigating
1. Session 1: Introduction to ChatGPT
2. Overview of natural language processing (NLP) and chatbots
3. Introduction to ChatGPT and its capabilities
4. Setting up the Python environment for ChatGPT
5. Basic usage of OpenAI’s API for ChatGPT
6. Session 2: Working with OpenAI API
7. Understanding the OpenAI API key and authentication
8. Exploring API endpoints for ChatGPT
9. Sending requests to the API and handling responses
10. Limitations and usage guidelines for the API
11. Session 3: Text Generation Basics
12. Basic text generation using ChatGPT
13. Input formats and techniques for generating coherent responses
14. Handling different prompt styles for varied outputs
15. Experimenting with temperature and max tokens for output control
16. Session 4: Advanced Text Generation
17. Fine-tuning prompts for specific tasks or contexts
18. Customizing responses with user-provided context
19. Controlling verbosity and length of generated text
20. Handling multiple turns in a conversation
21. Session 5: Error Handling and Model Behavior
22. Understanding and handling model biases
23. Dealing with inappropriate or biased outputs
24. Implementing error handling for API requests
25. Managing user interactions and providing feedback
26. Session 6: Integrating ChatGPT into Applications
27. Building a simple chatbot application with ChatGPT
28. Integrating ChatGPT into web applications or scripts
29. Handling user input and managing conversations
30. Exploring real-world use cases for ChatGPT
31. Session 7: Optimization and Performance
32. Strategies for optimizing API usage and cost
33. Caching and storing responses for efficiency
34. Implementing rate limiting and error handling
35. Discussing best practices for production use
36. Session 8: Project and Recap
37. Developing a small project using ChatGPT
38. Presenting and discussing projects within the group
39. Recap of key concepts and best practices
40. Q&A session and exploring further resources
Programming Languages & Tools
Certificates
Obtaining Your Certification
Upon successful completion of any course at Etlhive, participants receive a certificate attesting to their proficiency in the respective subject matter. These certificates serve as tangible evidence of the skills acquired during the training, enhancing the credibility of individuals in the job market and validating their expertise to potential employers. Etlhive certificates are recognized for their industry relevance and are highly regarded by leading organizations, providing a competitive edge to certificate holders. The validation process ensures that the certifications are earned through rigorous learning and assessment methods, reflecting real-world application and mastery of the concepts taught. With Etlhive certificates, individuals can showcase their commitment to continuous learning and professional development, opening doors to new career opportunities and advancement prospects.
Training students for leading brands
Frequently asked questions
Two types: Descriptive(focuses on past data to understand past/present trends) and Predictive(focuses on past data to predict unknown/future)..
Data analyst generally works on creation of reports based on company’s data driven KPI’s(generally involves descriptive analytics), whereas Data scientists understand business and domain along with the technicalities to understand what will happen in future(more on descriptive + predictive analytics both)
Â
Machine Learning algorithms are mathematical in nature, hence you need to first understand that part(includes statistics, probability theory, Linear Algebra).
Once you know this part, then in order to implement these algorithms on a real life data set, you need a language which contains modules which simplify ML development(like MATLAB, Python, R etc)
So, to sum it up:
-Maths
-Programming
-Data manipulation/preprocessing
-Machine Learning Algos
-Lots of scenarios
-End to End projects
-Deployment of Models on various cloud platforms.
Every person who has done some online course or has went through some tutorial uploads a CV for a JOB. But not necessary everyone gets it.
In order for you to get jobs, your skill level has to be detailed, your knowledge cannot be limited to generics. If you feel that by doing some sort of a crash course will get you anywhere, then I wish you luck.
By the time you have completed the course you should be able to handle complex scenarios with efficiency and measured approach(without any help of course).
So a simple advice: Join a detailed course.
The answer to this is relative based on your previous experience and technology. Kindly have a word with our technical counsellor regarding the same.
The term Data Science is used interchangeably with Datalogy.
Data Science employs its theories and techniques from physics, mathematics, nanotechnologies and this list goes to 23 fields.
Data Science is considered to be a part of many academic and research areas.
Data Science has been employed in fraud monitoring and security.
Data Analytics is now increasingly used in multiple sectors and these sectors owe their success to Data Science and Data Analytics
Various companies have certifications available for these kind of programs:
AWS Certified Machine Learning – Specialty certification
Professional Data Engineer Certification(Google)
Google Data Analytics Professional Certificate
Microsoft Certified: Azure Data Scientist Associate
Data science professionals(IBM)
and the list goes on.
Graduation in any stream, Freshers or working professionals who either wish to start their career as a Data Scientist or wish to switch from their previous profile into mainstream analytics.
Testimonials
"I have enrolled for the data science course here and it is the best institute for this course. The trainer, Vaibhav Bajaj is a data science expert who has used the algorithms he teaches in real-world projects so you can be sure to learn the right way of doing this. The best thing is, He will teach you the basics very well until you don't understand them and give you the proper knowledge for becoming a data scientist or analyst.
"My name is Akshat Goyal. I have done BE Mechanical in electrical. the purpose of doing this course is that I didn’t know the ABC of coding. In this course my mentor was Vaibhav Bajaj sir, and he is very experienced person in the eld of AI and ML. My experience was great with Vaibhav sir and he’s teaching style is awesome because he performs all practical by manually and give the real-life related examples, if I asked any doubt, instantly he cleared my all doubts. The best thing about this ETLHIVE Institute is that covered all missing session and provides full response and guidance after completing the course."
"It is a nice learning experience with ETLHIVE. Vaibhav sir has in-depth knowledge of data science and explains the concepts in a simple way. Each session comprises of practical as well which helps in getting hands-on practice in the classroom itself. Also, he provides latest interview questions/updates happening in the industry which helps to prepare us for the interviews and motivates us. Great job sir!!!"
I completed Data Science with Python under Vaibhav Sir. Although the duration of the course was more than the expected time, the trainer was very interactive and gave ample time to
come up with any doubts during session. The trainer has very deep knowledge of the concepts and ensures that all the queries are answered. Overall great learning experience. Surely recommend for people who want to enter Data Science. Thank you, Vaibhav sir, for huge amount of data science knowledge
I am a student of Data Science at this institute. Vaibhav Sir's teaching technique is excellent and well formed. He explains every topic with minute details by covering theory as well as practical session. We got revision session as well for most of the complex topics.
"I have completed an 8-month online data science course from the Etlhive-Wakad in Pune city. I took their structured online course to get me into the IT eld, and I am very satis ed and proud of my decision. It is the best online course.
Social Media Posts
